A Knowledge Discovery Framework for Civil Infrastructure Contexts

建议：CMS-9987871主要研究者：James Garrett研究机构：卡内基梅隆大学日期：2001年4月20日摘要CMS-9987871“民用基础设施背景下的知识发现框架”主要研究者：加勒特，James，卡内基梅隆大学;和苏·麦克尼尔这项研究的主要目标是专门研究抽象的CRISP-DM（跨行业数据挖掘标准流程）流程模型正在数据库知识发现（KDD）社区内开发，成为民用基础设施问题领域使用的框架。 近年来，随着领域数据的积累，民用基础设施研究人员已经转向数据密集型技术，以帮助他们了解退化机制和使用模式。 在大约同一时期，机器学习、数据库和统计社区的研究人员开始开发一套新的工具和技术，称为CRISP-DM，用于分析非常大的数据库。 该框架将帮助民用基础设施研究人员系统地应用CRISP-DM流程来满足其数据分析需求。 这样一个框架对于民用基础设施研究人员来说至关重要，因为他们开始分析他们收集的大量基础设施数据。 研究团队将使用CRISP-DM流程确定分析民用基础设施的初步案例研究。 案例研究将根据其KDD问题特征的独特性进行选择。 将特别注意那些在数据质量和数据编制方面提出挑战性问题的案例研究，这些案例研究是这一过程中最耗时和最困难的阶段，也是研究最少的阶段。 研究团队将根据CRISP-DM流程特征对民用基础设施数据分析需求进行分类。 该过程的所有阶段都将得到解决，但数据理解（包括数据质量），数据准备和建模阶段将得到深入处理。 然后，研究团队将开发一个更具体的框架，将CRISP-DM流程应用于民用基础设施分析需求。 研究小组还将确定和进行案例研究，以验证该框架。 最后，将在最后一年完成基于这项研究的网络课程和网络资料库的开发和部署。

Proposal: CMS-9987871PI: James GarrettInstitution: Carnegie Mellon UniversityDate: April 20, 2001ABSTRACT CMS-9987871 " A Knowledge Discovery Framework for Civil Infrastructure Contexts" PI: Garrett, James, Carnegie Mellon University; Christos Faloutsos, Carnegie Mellon University; and Sue McNeil, University of Illinois at Chicago.The primary objective of this research is to specialize the abstract CRISP-DM (Cross-Industry Standard Process for Data Mining) process model being developed within the Knowledge Discovery in Databases (KDD) community, into a framework for use in civil infrastructure problem domains. With the recent accumulation of domain data, civil infrastructrue researchers have turned to data-intensive techniques to aid their understanding of deterioration mechanisms and usage patterns. During roughly the same time period, researchers in the machine learning, database, and statistical communities began to develop a set of new tools and techniques, known as CRISP-DM, to analyze very large databases. This framework will assist civil infrastructure researchers in systematically applying the CRISP-DM process for their data analysis needs. Such a framework will become vital for civil infrastructure researchers as they begin to analyze the enormous amounts of infrastructure data they have collected. The research team will identify the analyze preliminary case studies in civil infrastructure using the CRISP-DM process. Case studies will be chosen based on the uniqueness of their KDD problem characteristics. Special attention will be paid to those case studies that present challenging issues in data quality and data preparation, the most time-consuming and difficult stages of the process as well as the least-studied. The research team will classify civil infrastructure data analysis needs in terms of CRISP-DM process characteristics. All phases of the process will be addressed, but the data understanding (including data quality), data preparation, and modeling phases will be treated in-depth. The research team will then develop a more specific framework for applying the CRISP-DM process to civil infrastructure analysis needs. The research team will also identify and conduct case studies with which to validate the framework. Finally, the development and deployment of a web-based course and a web repository based on this research will be completed during the final year.